531 research outputs found
Experimental Demonstration of Deterministic Chaos in a Waste Oil Biodiesel Semi-Industrial Furnace Combustion System
In this paper, the nonlinear dynamic characteristics of the oxygen-enriched combustion of waste oil biodiesel in semi-industrial furnaces were tested by the power spectrum, phase space reconstruction, the largest Lyapunov exponents, and the 0-1 test method. To express the influences of the system parameters, experiments were carried out under different oxygen content conditions (21%, 25%, 28%, 31%, and 33%). Higher oxygen enrichment degrees contribute to finer combustion sufficiency, which produces flames with high luminance. Flame luminance and temperature can be represented by different gray scale values of flame images. The chaotic characteristics of gray scale time series under different oxygen enrichment degrees were studied. With increased oxygen content, the chaotic characteristics of flame gradually developed from weak chaos to strong chaos. Furthermore, the flame maintained a stable combustion process in a high-temperature region. The stronger the chaotic characteristics of the flame, the better the combustion effect. It can be seen that the change of initial combustion conditions has a great influence on the whole combustion process. The results of several chaotic test methods were consistent. Using chaotic characteristics to analyze the waste oil biodiesel combustion process can digitize the combustion process, find the best combustion state, optimize, and precisely control it
Temporal Knowledge Graph Completion: A Survey
Knowledge graph completion (KGC) can predict missing links and is crucial for
real-world knowledge graphs, which widely suffer from incompleteness. KGC
methods assume a knowledge graph is static, but that may lead to inaccurate
prediction results because many facts in the knowledge graphs change over time.
Recently, emerging methods have shown improved predictive results by further
incorporating the timestamps of facts; namely, temporal knowledge graph
completion (TKGC). With this temporal information, TKGC methods can learn the
dynamic evolution of the knowledge graph that KGC methods fail to capture. In
this paper, for the first time, we summarize the recent advances in TKGC
research. First, we detail the background of TKGC, including the problem
definition, benchmark datasets, and evaluation metrics. Then, we summarize
existing TKGC methods based on how timestamps of facts are used to capture the
temporal dynamics. Finally, we conclude the paper and present future research
directions of TKGC
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